Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.展开更多
The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conv...The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conventional methods both costly and inefficient.Recently,Artificial Intelligence(AI)has gained prominence for developing automated systems that can accurately diagnose or segment brain tumors in a shorter time frame.Many researchers have examined various algorithms that provide both speed and accuracy in detecting and classifying brain tumors.This paper proposes a newmodel based on AI,called the Brain Tumor Detection(BTD)model,based on brain tumor Magnetic Resonance Images(MRIs).The proposed BTC comprises three main modules:(i)Image Processing Module(IPM),(ii)Patient Detection Module(PDM),and(iii)Explainable AI(XAI).In the first module(i.e.,IPM),the used dataset is preprocessed through two stages:feature extraction and feature selection.At first,the MRI is preprocessed,then the images are converted into a set of features using several feature extraction methods:gray level co-occurrencematrix,histogramof oriented gradient,local binary pattern,and Tamura feature.Next,the most effective features are selected fromthese features separately using ImprovedGrayWolfOptimization(IGWO).IGWOis a hybrid methodology that consists of the Filter Selection Step(FSS)using information gain ratio as an initial selection stage and Binary Gray Wolf Optimization(BGWO)to make the proposed method better at detecting tumors by further optimizing and improving the chosen features.Then,these features are fed to PDM using several classifiers,and the final decision is based on weighted majority voting.Finally,through Local Interpretable Model-agnostic Explanations(LIME)XAI,the interpretability and transparency in decision-making processes are provided.The experiments are performed on a publicly available Brain MRI dataset that consists of 98 normal cases and 154 abnormal cases.During the experiments,the dataset was divided into 70%(177 cases)for training and 30%(75 cases)for testing.The numerical findings demonstrate that the BTD model outperforms its competitors in terms of accuracy,precision,recall,and F-measure.It introduces 98.8%accuracy,97%precision,97.5%recall,and 97.2%F-measure.The results demonstrate the potential of the proposed model to revolutionize brain tumor diagnosis,contribute to better treatment strategies,and improve patient outcomes.展开更多
Abstract--This paper provides a survey on modeling and theories of networked control systems （NCS）. In the first part, modeling of the different types of imperfections that affect NCS is discussed. These imperfectio...Abstract--This paper provides a survey on modeling and theories of networked control systems （NCS）. In the first part, modeling of the different types of imperfections that affect NCS is discussed. These imperfections are quantization errors, packet dropouts, variable sampling/transmission intervals, vari- able transmission delays, and communication constraints. Then follows in the second part a presentation of several theories that have been applied for controlling networked systems. These theories include： input delay system approach, Markovian system approach, switched system approach, stochastic system approach, impulsive system approach, and predictive control approach. In the last part, some advanced issues in NCS including decentral- ized and distributed NCS, cloud control system, and co-design of NCS are reviewed. Index Terms--Decentralized networked control systems （NCS）, distributed networked control systems, network constraints, net- worked control system, quantization, time delays.展开更多
The wind turbine(WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms.This paper focuses on wind turbine control des...The wind turbine(WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms.This paper focuses on wind turbine control design strategies.The content is divided into the following parts: 1) An overview of the recent advances that have been made in the application of adaptive and model predictive control strategies for wind turbines. 2) Summarizes some important aspects of modeling of wind turbines for control studies. 3) Provides an outlook on the application of adaptive model predictive control for uncertain systems to stimulate new research interests for wind turbine systems. We provide an overall picture of the research results with evaluation of the merits/demerits.展开更多
The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wid...The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, hut benchmarking would give greater confidence. Technical challenges confrontingprocess systems engineers in developing enabling tools and techniques are discussed regarding flexibilityand uncertainty, responsiveness and agility, robustness and security, the prediction of mixture propertiesand function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to driveagility will require tackling new challenges, such as how to ensure the consistency and confidentiality ofdata through long and complex supply chains. Modeling challenges also exist, and involve ensuring that allkey aspects are properly modeled, particularly where health, safety, and environmental concerns requireaccurate predictions of small but critical amounts at specific locations. Environmental concerns will requireus to keep a closer track on all molecular species so that they are optimally used to create sustainablesolutions. Disruptive business models may result, particularly from new personalized products, but that isdifficult to predict.展开更多
This paper examines a consensus problem in multiagent discrete-time systems, where each agent can exchange information only from its neighbor agents. A decentralized protocol is designed for each agent to steer all ag...This paper examines a consensus problem in multiagent discrete-time systems, where each agent can exchange information only from its neighbor agents. A decentralized protocol is designed for each agent to steer all agents to the same vector. The design condition is expressed in the form of a linear matrix inequality. Finally, a simulation example is presented and a comparison is made to demonstrate the effectiveness of the developed methodology.展开更多
This paper addresses an infinite horizon distributed H2/H∞ filtering for discrete-time systems under conditions of bounded power and white stochastic signals. The filter algorithm is designed by computing a pair of g...This paper addresses an infinite horizon distributed H2/H∞ filtering for discrete-time systems under conditions of bounded power and white stochastic signals. The filter algorithm is designed by computing a pair of gains namely the estimator and the coupling. Herein, we implement a filter to estimate unknown parameters such that the closed-loop multi-sensor accomplishes the desired performances of the proposed H2 and H∞ schemes over a finite horizon. A switched strategy is implemented to switch between the states once the operation conditions have changed due to disturbances. It is shown that the stability of the overall filtering-error system with H2/H∞ performance can be established if a piecewise-quadratic Lyapunov function is properly constructed. A simulation example is given to show the effectiveness of the proposed approach.展开更多
Microgrid has emerged as an answer to growing demand for distributed generation(DG) in power systems. It contains several DG units including microalternator, photovoltaic system and wind generation. It turns out that ...Microgrid has emerged as an answer to growing demand for distributed generation(DG) in power systems. It contains several DG units including microalternator, photovoltaic system and wind generation. It turns out that sustained operation relies on the stability of these constituent systems. In this paper, a microgrid consisting of microalternator and photovoltaic system is modeled as a networked control system of systems(So S)subjected to packet dropouts and delays. Next, an observerbased controller is designed to stabilize the system in presence of the aforementioned communication constraints and simulation results are provided to support the control design methodology.展开更多
SU（1,1） dynamical symmetry is of fundamental importance in analyzing unbounded quantum systems in theoretical and applied physics. In this paper, we study the control of generalized coherent states associated with q...SU（1,1） dynamical symmetry is of fundamental importance in analyzing unbounded quantum systems in theoretical and applied physics. In this paper, we study the control of generalized coherent states associated with quantum systems with SU（1,1） dynamical symmetry. Based on a pseudo Riemannian metric on the SU（1,1） group, we obtain necessary conditions for minimizing the field fluence of controls that steer the system to the desired final state. Further analyses show that the candidate optimal control solutions can be classified into normal and abnormal extremals. The abnormal extremals can only be constant functions when the control Hamiltonian is non-parabolic, while the normal extremals can be expressed by Weierstrass elliptic functions according to the parabolicity of the control Hamiltonian. As a special case, the optimal control solution that maximally squeezes a generalized coherent state is a sinusoidal field, which is consistent with what is used in the laboratory.展开更多
The problem of robust H∞ control for uncertain discrete-time Takagi and Sugeno (T-S) fuzzy networked control systems (NCSs) is investigated in this paper subject to state quantization. By taking into consideration ne...The problem of robust H∞ control for uncertain discrete-time Takagi and Sugeno (T-S) fuzzy networked control systems (NCSs) is investigated in this paper subject to state quantization. By taking into consideration network induced delays and packet dropouts, an improved model of network-based control is developed. A less conservative delay-dependent stability condition for the closed NCSs is derived by employing a fuzzy Lyapunov-Krasovskii functional. Robust H∞ fuzzy controller is constructed that guarantee asymptotic stabilization of the NCSs and expressed in LMI-based conditions. A numerical example illustrates the effectiveness of the developed technique.展开更多
This paper introduces the mathematical model of ammonia and urea reactors and suggested three methods for designing a special purpose controller. The first proposed method is Adaptive model predictive controller, the ...This paper introduces the mathematical model of ammonia and urea reactors and suggested three methods for designing a special purpose controller. The first proposed method is Adaptive model predictive controller, the second is Adaptive Neural Network Model Predictive Control, and the third is Adaptive neuro-fuzzy sliding mode controller. These methods are applied to a multivariable nonlinear system as an ammonia–urea reactor system. The main target of these controllers is to achieve stabilization of the outlet concentration of ammonia and urea, a stable reaction rate, an increase in the conversion of carbon monoxide(CO) into carbon dioxide(CO_2) to reduce the pollution effect, and an increase in the ammonia and urea productions, keeping the NH_3/CO_2 ratio equal to 3 to reduce the unreacted CO_2 and NH_3, and the two reactors' temperature in the suitable operating ranges due to the change in reactor parameters or external disturbance. Simulation results of the three controllers are compared. Comparative analysis proves the effectiveness of the suggested Adaptive neurofuzzy sliding mode controller than the two other controllers according to external disturbance and the change of parameters. Moreover, the suggested methods when compared with other controllers in the literature show great success in overcoming the external disturbance and the change of parameters.展开更多
The advances in MIMO systems and networking technologies introduced a revolution in recent times, especially in wireless and wired multi-cast (multi-point-to-multi-point) transmission field. In this work, the distribu...The advances in MIMO systems and networking technologies introduced a revolution in recent times, especially in wireless and wired multi-cast (multi-point-to-multi-point) transmission field. In this work, the distributed versions of self-tuning proportional integral plus derivative (SPID) controller and self-tuning proportional plus integral (SPI) controller are described. An explicit rate feedback mechanism is used to design a controller for regulating the source rates in wireless and wired multi-cast networks. The control parameters of the SPID and SPI controllers are determined to ensure the stability of the control loop. Simulations are carried out with wireless and wired multi-cast models, to evaluate the performance of the SPID and SPI controllers and the ensuing results show that SPID scheme yields better performance than SPI scheme;however, it requires more computing time and central processing unit (CPU) resources.展开更多
The analytical capacity of massive data has become increasingly necessary, given the high volume of data that has been generated daily by different sources. The data sources are varied and can generate a huge amount o...The analytical capacity of massive data has become increasingly necessary, given the high volume of data that has been generated daily by different sources. The data sources are varied and can generate a huge amount of data, which can be processed in batch or stream settings. The stream setting corresponds to the treatment of a continuous sequence of data that arrives in real-time flow and needs to be processed in real-time. The models, tools, methods and algorithms for generating intelligence from data stream culminate in the approaches of Data Stream Mining and Data Stream Learning. The activities of such approaches can be organized and structured according to Engineering principles, thus allowing the principles of Analytical Engineering, or more specifically, Analytical Engineering for Data Stream (AEDS). Thus, this article presents the AEDS conceptual framework composed of four pillars (Data, Model, Tool, People) and three processes (Acquisition, Retention, Review). The definition of these pillars and processes is carried out based on the main components of data stream setting, corresponding to four pillars, and also on the necessity to operationalize the activities of an Analytical Organization (AO) in the use of AEDS four pillars, which determines the three proposed processes. The AEDS framework favors the projects carried out in an AO, that is, its Analytical Projects (AP), to favor the delivery of results, or Analytical Deliverables (AD), carried out by the Analytical Teams (AT) in order to provide intelligence from stream data.展开更多
In the face of an escalating global water crisis,countries worldwide grapple with the crippling effects of scarcity,jeopardizing economic progress and hindering societal advancement.Solar energy emerges as a beacon of...In the face of an escalating global water crisis,countries worldwide grapple with the crippling effects of scarcity,jeopardizing economic progress and hindering societal advancement.Solar energy emerges as a beacon of hope,offering a sustainable and environmentally friendly solution to desalination.Solar distillation technology,harnessing the power of the sun,transforms seawater into freshwater,expanding the availability of this precious resource.Optimizing solar still performance under specific climatic conditions and evaluating different configurations is crucial for practical implementation and widespread adoption of solar energy.In this study,we conducted theoretical investigations on three distinct solar still configurations to evaluate their performance under Baghdad’s climatic conditions.The solar stills analyzed include the passive solar still,themodified solar still coupled with a magnetic field,and themodified solar still coupled with bothmagnetic and electrical fields.The results proved that the evaporation heat transfer coefficient peaked at 14:00,reaching 25.05 W/m^(2).℃for the convention pyramid solar still(CPSS),32.33 W/m^(2).℃for the magnetic pyramid solar still(MPSS),and 40.98 W/m^(2).℃for elecro-magnetic pyramid solar still(EMPSS),highlighting their efficiency in converting solar energy to vapor.However,exergy efficiency remained notably lower,at 1.6%,5.31%,and 7.93%for the three still types,even as energy efficiency reached its maximum of 18.6%at 14:00 with a corresponding peak evaporative heat of 162.4 W/m^(2).展开更多
The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware identification.Using ...The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware identification.Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware classification.This technique optimizes the model’s performance and reduces computational requirements.The proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature vector.Through the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate classification.The evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced features.This demonstrates the method’s ability to classify malware samples accurately while minimizing processing time.The method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and prediction.The new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and efficiency.The research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained devices.Novelty and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.展开更多
An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent cros...An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent crossover outperforms the traditional genetic algorithm combined with a rule-based approach utilizing domain specific knowledge developed by Webb, et al. [1]. The encoding of the improved crossover consists of two chromosome strings within the genetic algorithm where the first string represents the design or solution string, and the second string represents chromosome crossover string intelligence. This improved crossover methodology saves the best population members or designs evaluated from each generation and applies crossover chromosome intelligence to the best saved population members paired with globally selected parents. Enhanced features of this crossover methodology employ the random selection of the best designs from the prior generation as a potential parent coupled with alternating intelligence pairing methods. In addition to this approach, two globally selected parents possess the ability to mate utilizing crossover chromosome string intelligence maintaining the integrity of a global GA search. Overall, the final population following crossover employs both global and best generation design chromosome strings to maximize creativity while enhancing the solution search. This is a modification to a conventional GA that can be translated into GA encoding. This technique is explored initially through a Base 10 mathematical application followed by the examination of plate structural optimization considering stress and displacement constraints. Results from crossover intelligence are compared with the conventional genetic algorithm and from Webb, et al. [1] which illustrates the outcome of a two phase genetic optimization algorithm.展开更多
Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequ...Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.展开更多
Considering the improved interpretable performance of Kolmogorov–Arnold Networks(KAN)algorithm compared to multi-layer perceptron(MLP)algorithm,a fundamental research question arises on how modifying the loss functio...Considering the improved interpretable performance of Kolmogorov–Arnold Networks(KAN)algorithm compared to multi-layer perceptron(MLP)algorithm,a fundamental research question arises on how modifying the loss function of KAN affects its modelling performance for energy systems,particularly industrial-scale thermal power plants.In this regard,first,we modify the loss function of both KAN and MLP algorithms and embed Pearson Correlation Coefficient(PCC).Second,the algorithmic configurations built on PCC,i.e.,KAN_PCC and MLP_PCC as well as original architecture of KAN and MLP are deployed for modelling and optimisation analyses for two case studies of energy systems:(i)energy efficiency cooling and energy efficiency heating of buildings,and(ii)power generation operation of 660 MW capacity thermal power plant.The analysis reveals superior modelling performance of KAN and KAN_PCC algorithms than those of MLP and MLP_PCC for the two case studies.KAN models are embedded in the optimisation framework of nonlinear programming and feasible optimal solutions are estimated,maximising thermal efficiency up to 42.17±0.88%and minimising turbine heat rate to 7487±129 kJ/kWh corresponding to power generation of 500±14 MW for the thermal power plant.It is anticipated that the scientific,research and industrial community may benefit from the fundamental insights presented in this paper for the ML algorithm selection and carrying out model-based optimisation analysis for the performance enhancement of energy systems.展开更多
A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm...A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.展开更多
This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize t...This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize to the state of a command multi-agent dynamical systems, where pinning control is used generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents＇ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.展开更多
文摘Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
文摘The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conventional methods both costly and inefficient.Recently,Artificial Intelligence(AI)has gained prominence for developing automated systems that can accurately diagnose or segment brain tumors in a shorter time frame.Many researchers have examined various algorithms that provide both speed and accuracy in detecting and classifying brain tumors.This paper proposes a newmodel based on AI,called the Brain Tumor Detection(BTD)model,based on brain tumor Magnetic Resonance Images(MRIs).The proposed BTC comprises three main modules:(i)Image Processing Module(IPM),(ii)Patient Detection Module(PDM),and(iii)Explainable AI(XAI).In the first module(i.e.,IPM),the used dataset is preprocessed through two stages:feature extraction and feature selection.At first,the MRI is preprocessed,then the images are converted into a set of features using several feature extraction methods:gray level co-occurrencematrix,histogramof oriented gradient,local binary pattern,and Tamura feature.Next,the most effective features are selected fromthese features separately using ImprovedGrayWolfOptimization(IGWO).IGWOis a hybrid methodology that consists of the Filter Selection Step(FSS)using information gain ratio as an initial selection stage and Binary Gray Wolf Optimization(BGWO)to make the proposed method better at detecting tumors by further optimizing and improving the chosen features.Then,these features are fed to PDM using several classifiers,and the final decision is based on weighted majority voting.Finally,through Local Interpretable Model-agnostic Explanations(LIME)XAI,the interpretability and transparency in decision-making processes are provided.The experiments are performed on a publicly available Brain MRI dataset that consists of 98 normal cases and 154 abnormal cases.During the experiments,the dataset was divided into 70%(177 cases)for training and 30%(75 cases)for testing.The numerical findings demonstrate that the BTD model outperforms its competitors in terms of accuracy,precision,recall,and F-measure.It introduces 98.8%accuracy,97%precision,97.5%recall,and 97.2%F-measure.The results demonstrate the potential of the proposed model to revolutionize brain tumor diagnosis,contribute to better treatment strategies,and improve patient outcomes.
基金supported by the Deanship of Scientific Research(DSR) at KFUPM through Research Project(IN141048)
文摘Abstract--This paper provides a survey on modeling and theories of networked control systems （NCS）. In the first part, modeling of the different types of imperfections that affect NCS is discussed. These imperfections are quantization errors, packet dropouts, variable sampling/transmission intervals, vari- able transmission delays, and communication constraints. Then follows in the second part a presentation of several theories that have been applied for controlling networked systems. These theories include： input delay system approach, Markovian system approach, switched system approach, stochastic system approach, impulsive system approach, and predictive control approach. In the last part, some advanced issues in NCS including decentral- ized and distributed NCS, cloud control system, and co-design of NCS are reviewed. Index Terms--Decentralized networked control systems （NCS）, distributed networked control systems, network constraints, net- worked control system, quantization, time delays.
基金supported by the Deanship of Scientific Research(DSR)at KFUPM Through Distinguished Professorship Research(IN161065)
文摘The wind turbine(WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms.This paper focuses on wind turbine control design strategies.The content is divided into the following parts: 1) An overview of the recent advances that have been made in the application of adaptive and model predictive control strategies for wind turbines. 2) Summarizes some important aspects of modeling of wind turbines for control studies. 3) Provides an outlook on the application of adaptive model predictive control for uncertain systems to stimulate new research interests for wind turbine systems. We provide an overall picture of the research results with evaluation of the merits/demerits.
文摘The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, hut benchmarking would give greater confidence. Technical challenges confrontingprocess systems engineers in developing enabling tools and techniques are discussed regarding flexibilityand uncertainty, responsiveness and agility, robustness and security, the prediction of mixture propertiesand function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to driveagility will require tackling new challenges, such as how to ensure the consistency and confidentiality ofdata through long and complex supply chains. Modeling challenges also exist, and involve ensuring that allkey aspects are properly modeled, particularly where health, safety, and environmental concerns requireaccurate predictions of small but critical amounts at specific locations. Environmental concerns will requireus to keep a closer track on all molecular species so that they are optimally used to create sustainablesolutions. Disruptive business models may result, particularly from new personalized products, but that isdifficult to predict.
基金supported by Deanship of Scientific research(CDSR)at KFUPM(RG-1316-1)
文摘This paper examines a consensus problem in multiagent discrete-time systems, where each agent can exchange information only from its neighbor agents. A decentralized protocol is designed for each agent to steer all agents to the same vector. The design condition is expressed in the form of a linear matrix inequality. Finally, a simulation example is presented and a comparison is made to demonstrate the effectiveness of the developed methodology.
基金supported by the Deanship of Scientific Research(DSR)at KFUPM through distinguished professorship project(161065)
文摘This paper addresses an infinite horizon distributed H2/H∞ filtering for discrete-time systems under conditions of bounded power and white stochastic signals. The filter algorithm is designed by computing a pair of gains namely the estimator and the coupling. Herein, we implement a filter to estimate unknown parameters such that the closed-loop multi-sensor accomplishes the desired performances of the proposed H2 and H∞ schemes over a finite horizon. A switched strategy is implemented to switch between the states once the operation conditions have changed due to disturbances. It is shown that the stability of the overall filtering-error system with H2/H∞ performance can be established if a piecewise-quadratic Lyapunov function is properly constructed. A simulation example is given to show the effectiveness of the proposed approach.
基金supported by the Deanship for Scientific Research(DSR)at KFUPM through Distinguished Professorship Research Project(IN-141003)
文摘Microgrid has emerged as an answer to growing demand for distributed generation(DG) in power systems. It contains several DG units including microalternator, photovoltaic system and wind generation. It turns out that sustained operation relies on the stability of these constituent systems. In this paper, a microgrid consisting of microalternator and photovoltaic system is modeled as a networked control system of systems(So S)subjected to packet dropouts and delays. Next, an observerbased controller is designed to stabilize the system in presence of the aforementioned communication constraints and simulation results are provided to support the control design methodology.
基金supported by the National Natural Science Foundation of China(Nos.61374091,61134008)
文摘SU（1,1） dynamical symmetry is of fundamental importance in analyzing unbounded quantum systems in theoretical and applied physics. In this paper, we study the control of generalized coherent states associated with quantum systems with SU（1,1） dynamical symmetry. Based on a pseudo Riemannian metric on the SU（1,1） group, we obtain necessary conditions for minimizing the field fluence of controls that steer the system to the desired final state. Further analyses show that the candidate optimal control solutions can be classified into normal and abnormal extremals. The abnormal extremals can only be constant functions when the control Hamiltonian is non-parabolic, while the normal extremals can be expressed by Weierstrass elliptic functions according to the parabolicity of the control Hamiltonian. As a special case, the optimal control solution that maximally squeezes a generalized coherent state is a sinusoidal field, which is consistent with what is used in the laboratory.
文摘The problem of robust H∞ control for uncertain discrete-time Takagi and Sugeno (T-S) fuzzy networked control systems (NCSs) is investigated in this paper subject to state quantization. By taking into consideration network induced delays and packet dropouts, an improved model of network-based control is developed. A less conservative delay-dependent stability condition for the closed NCSs is derived by employing a fuzzy Lyapunov-Krasovskii functional. Robust H∞ fuzzy controller is constructed that guarantee asymptotic stabilization of the NCSs and expressed in LMI-based conditions. A numerical example illustrates the effectiveness of the developed technique.
文摘This paper introduces the mathematical model of ammonia and urea reactors and suggested three methods for designing a special purpose controller. The first proposed method is Adaptive model predictive controller, the second is Adaptive Neural Network Model Predictive Control, and the third is Adaptive neuro-fuzzy sliding mode controller. These methods are applied to a multivariable nonlinear system as an ammonia–urea reactor system. The main target of these controllers is to achieve stabilization of the outlet concentration of ammonia and urea, a stable reaction rate, an increase in the conversion of carbon monoxide(CO) into carbon dioxide(CO_2) to reduce the pollution effect, and an increase in the ammonia and urea productions, keeping the NH_3/CO_2 ratio equal to 3 to reduce the unreacted CO_2 and NH_3, and the two reactors' temperature in the suitable operating ranges due to the change in reactor parameters or external disturbance. Simulation results of the three controllers are compared. Comparative analysis proves the effectiveness of the suggested Adaptive neurofuzzy sliding mode controller than the two other controllers according to external disturbance and the change of parameters. Moreover, the suggested methods when compared with other controllers in the literature show great success in overcoming the external disturbance and the change of parameters.
文摘The advances in MIMO systems and networking technologies introduced a revolution in recent times, especially in wireless and wired multi-cast (multi-point-to-multi-point) transmission field. In this work, the distributed versions of self-tuning proportional integral plus derivative (SPID) controller and self-tuning proportional plus integral (SPI) controller are described. An explicit rate feedback mechanism is used to design a controller for regulating the source rates in wireless and wired multi-cast networks. The control parameters of the SPID and SPI controllers are determined to ensure the stability of the control loop. Simulations are carried out with wireless and wired multi-cast models, to evaluate the performance of the SPID and SPI controllers and the ensuing results show that SPID scheme yields better performance than SPI scheme;however, it requires more computing time and central processing unit (CPU) resources.
文摘The analytical capacity of massive data has become increasingly necessary, given the high volume of data that has been generated daily by different sources. The data sources are varied and can generate a huge amount of data, which can be processed in batch or stream settings. The stream setting corresponds to the treatment of a continuous sequence of data that arrives in real-time flow and needs to be processed in real-time. The models, tools, methods and algorithms for generating intelligence from data stream culminate in the approaches of Data Stream Mining and Data Stream Learning. The activities of such approaches can be organized and structured according to Engineering principles, thus allowing the principles of Analytical Engineering, or more specifically, Analytical Engineering for Data Stream (AEDS). Thus, this article presents the AEDS conceptual framework composed of four pillars (Data, Model, Tool, People) and three processes (Acquisition, Retention, Review). The definition of these pillars and processes is carried out based on the main components of data stream setting, corresponding to four pillars, and also on the necessity to operationalize the activities of an Analytical Organization (AO) in the use of AEDS four pillars, which determines the three proposed processes. The AEDS framework favors the projects carried out in an AO, that is, its Analytical Projects (AP), to favor the delivery of results, or Analytical Deliverables (AD), carried out by the Analytical Teams (AT) in order to provide intelligence from stream data.
文摘In the face of an escalating global water crisis,countries worldwide grapple with the crippling effects of scarcity,jeopardizing economic progress and hindering societal advancement.Solar energy emerges as a beacon of hope,offering a sustainable and environmentally friendly solution to desalination.Solar distillation technology,harnessing the power of the sun,transforms seawater into freshwater,expanding the availability of this precious resource.Optimizing solar still performance under specific climatic conditions and evaluating different configurations is crucial for practical implementation and widespread adoption of solar energy.In this study,we conducted theoretical investigations on three distinct solar still configurations to evaluate their performance under Baghdad’s climatic conditions.The solar stills analyzed include the passive solar still,themodified solar still coupled with a magnetic field,and themodified solar still coupled with bothmagnetic and electrical fields.The results proved that the evaporation heat transfer coefficient peaked at 14:00,reaching 25.05 W/m^(2).℃for the convention pyramid solar still(CPSS),32.33 W/m^(2).℃for the magnetic pyramid solar still(MPSS),and 40.98 W/m^(2).℃for elecro-magnetic pyramid solar still(EMPSS),highlighting their efficiency in converting solar energy to vapor.However,exergy efficiency remained notably lower,at 1.6%,5.31%,and 7.93%for the three still types,even as energy efficiency reached its maximum of 18.6%at 14:00 with a corresponding peak evaporative heat of 162.4 W/m^(2).
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R435),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware identification.Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware classification.This technique optimizes the model’s performance and reduces computational requirements.The proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature vector.Through the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate classification.The evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced features.This demonstrates the method’s ability to classify malware samples accurately while minimizing processing time.The method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and prediction.The new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and efficiency.The research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained devices.Novelty and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.
文摘An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent crossover outperforms the traditional genetic algorithm combined with a rule-based approach utilizing domain specific knowledge developed by Webb, et al. [1]. The encoding of the improved crossover consists of two chromosome strings within the genetic algorithm where the first string represents the design or solution string, and the second string represents chromosome crossover string intelligence. This improved crossover methodology saves the best population members or designs evaluated from each generation and applies crossover chromosome intelligence to the best saved population members paired with globally selected parents. Enhanced features of this crossover methodology employ the random selection of the best designs from the prior generation as a potential parent coupled with alternating intelligence pairing methods. In addition to this approach, two globally selected parents possess the ability to mate utilizing crossover chromosome string intelligence maintaining the integrity of a global GA search. Overall, the final population following crossover employs both global and best generation design chromosome strings to maximize creativity while enhancing the solution search. This is a modification to a conventional GA that can be translated into GA encoding. This technique is explored initially through a Base 10 mathematical application followed by the examination of plate structural optimization considering stress and displacement constraints. Results from crossover intelligence are compared with the conventional genetic algorithm and from Webb, et al. [1] which illustrates the outcome of a two phase genetic optimization algorithm.
文摘Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.
文摘Considering the improved interpretable performance of Kolmogorov–Arnold Networks(KAN)algorithm compared to multi-layer perceptron(MLP)algorithm,a fundamental research question arises on how modifying the loss function of KAN affects its modelling performance for energy systems,particularly industrial-scale thermal power plants.In this regard,first,we modify the loss function of both KAN and MLP algorithms and embed Pearson Correlation Coefficient(PCC).Second,the algorithmic configurations built on PCC,i.e.,KAN_PCC and MLP_PCC as well as original architecture of KAN and MLP are deployed for modelling and optimisation analyses for two case studies of energy systems:(i)energy efficiency cooling and energy efficiency heating of buildings,and(ii)power generation operation of 660 MW capacity thermal power plant.The analysis reveals superior modelling performance of KAN and KAN_PCC algorithms than those of MLP and MLP_PCC for the two case studies.KAN models are embedded in the optimisation framework of nonlinear programming and feasible optimal solutions are estimated,maximising thermal efficiency up to 42.17±0.88%and minimising turbine heat rate to 7487±129 kJ/kWh corresponding to power generation of 500±14 MW for the thermal power plant.It is anticipated that the scientific,research and industrial community may benefit from the fundamental insights presented in this paper for the ML algorithm selection and carrying out model-based optimisation analysis for the performance enhancement of energy systems.
文摘A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.
基金supported by the Deanship of Scientific Research at King Fahd University of Petroleum & Minerals Project(No.JF141002)the National Science Foundation(No.ECCS-1405173)+3 种基金the Office of Naval Research(Nos.N000141310562,N000141410718)the U.S. Army Research Office(No.W911NF-11-D-0001)the National Natural Science Foundation of China(No.61120106011)the Project 111 from the Ministry of Education of China(No.B08015)
文摘This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize to the state of a command multi-agent dynamical systems, where pinning control is used generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents＇ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.
